Cognitive Robotics最新文献

Knee joint rehabilitation exercise refers to a therapeutic procedure of a patient having dysfunctions in certain abilities to move knee joint due to some medical conditions like trauma or paralysis. The exercise is basically a series of repeated assistive physical movements within the range of motion (RoM) of the joint. Reflex action of limbs during RoM exercise causes inappropriate balance of load which may cause secondary injuries, such as damages of muscle or tendon tissues. Establishing correlation between impedance data and limb motions is important to solve this problem. This paper aims to design and modeling of a robotic arm with an original approach in control strategy which is developed based on the correlation in between the joint-impedances and joint-motion characteristics during exercise. The knee joint impedances are estimated based on the internal feedback of the system dynamics, that lead to design the torque compensator to improve the overall control signals in real time. This paper also demonstrates the characteristics of various responses of the system during exercise with human subject. Results have reflected good performances with low position and velocity tracking errors, $\pm 0.{02}^{\circ }$ and $0.04rad.se{c}^{-1}$ during hold phase; and $\pm 0.{14}^{\circ }$ and $0.17rad.se{c}^{-1}$ during motion phse. Though, the limitation of the prototype is its current RoM (limited to $0^{\circ }$–${25}^{\circ }$), the system has potential in the application of RoM exercise for paraplegic or monoplegic patients.

In the realm of speech emotion recognition, researchers strive to refine representation methods for improved emotional information capture. Traditional one-dimensional time series classification falls short in expressing intricate emotional patterns present in speech signals, posing challenges in accuracy and robustness. This study introduces an innovative algorithm leveraging Hilbert curves to transform one-dimensional speech data into two-dimensional form, enhancing feature extraction accuracy. A tiling module based on Hilbert curve maximizes Hilbert curve arrangements for improved emotional information capture. Results reveal spatial efficiency gains up to 23,195 times pixel units, enhancing data storage. With an exceptional 98.73% accuracy, the proposed approach traditional methods, affirming its superior emotion classification performance on the same dataset. These empirical findings underscore the effectiveness of our proposed method in advancing speech emotion recognition.

These days, plastic trash is exponentially overwhelming our waterways. The catastrophe has attracted global attention at this point. As a result, protecting the environment on the water's surface has received increasing focus. Currently, manpower can be used to clean up contaminated water bodies like ponds, rivers, and oceans. Using the current cleaning approach results in low efficiency and hazard. The detection, collection, sorting, and removal of plastic trash from such water surfaces has been the subject of relatively little robotic research, despite the dire circumstances. From private sources, there are very few individual efforts to be found. In order to attain great efficiency without human assistance or operation, a fully autonomous water surface cleaning robot is proposed in this study. The robot was created to adapt to any type of water body found in the real world. An efficient object identification machine learning technique can be suggested for the creation of autonomous cleaning robots. This study improved the Single Short Detection (SSD) method to recognise objects accurately. Because of the enhanced detection techniques, the robot is able to collect trash on its own. With a mean average precision (mAP) of 94.099 % and a detection speed of up to 64.67 frames per second, experimental findings show that the enhanced SSD has exceptional detection speed and accuracy.

UAVs are often limited by limited resources when performing flight tasks, especially the contradiction between storage resources and computing resources when the huge YOLOv3 model is deployed on the edge UAVs. In this paper, we tend to compress YOLOv3 model in different aspects to achieve load availability at the edge. In this paper, deep separable convolution is introduced to reduce the computation of the model. Then, PR regularization term is used as the regularization term of sparse training to better distinguish scaling factors, and then the hybrid pruning combining channel pruning and layer pruning is carried out on the model according to scaling factors, in order to reduce the number of model parameters and the amount of calculation. Finally, since the training data is a 32-bit floating point number, DoReFa-Net quantization method is used to quantify the model, so as to compress the storage capacity of the model. The experimental results show that the compression scheme proposed in this paper can effectively reduce the number of parameters by 97.5 % and the calculation amount by 82.3 %, and can maintain the original detection efficiency of UAVs.

In recent years, the rapid industrialization of the world has led to an increasing importance of energy minerals. However, due to the scarcity of mineral resources, opportunities to rely on alternative energy are escalating. As a result, exploration of ocean resources, which exist abundantly in the sea, is being pursued. However, the manual exploration of ocean resources by diving and visually searching is dangerous and impractical. Therefore, it is pertinent to safely advance underwater exploration by having robots perform the work instead. In underwater environments, robots are commonly used as a mainstream exploration tool due to the various hazardous environmental conditions. However, there are several problems with controlling robots in underwater environments, and one of them is poor visibility underwater. Therefore, to improve visibility underwater, efforts are being made to achieve high resolution using super-resolution technology on underwater images. In this paper we first introduce the general model and architecture in GAN. Then we combine the GAN modal and characteristics of the underwater environment, elaborating how ESRGAN can be suitable for such circumstance. For data from ECCV2018 PIRM-SR, ESRGAN outperforms other traditional model like EnhanceNet [1], EDSR [2], RCAN [3], at least 24 % [4]. Such model can be equipped with robotics that highly depends on the resolution of the image, such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs).

The lightweight Unmanned Aerial Vehicle (UAV) flight activities are constrained, particularly in the UAV range or activity span and perseverance, by the strategic correspondence link capabilities. This paper tends to the different overlap issue of trading off a set of mission prerequisites, the UAV execution parameters, and strategic credibility; thus compromising between the communication load characterized by a crucial, communication link transmitting power necessities, power accessibility onboard UAV as a weight-restricted parameter, and the UAV security.

Revenue loss is a major issue for any country. Conversion of this loss into utilization would prove to be a huge benefit to the country. In view of this fact, the economic load dispatch problem draws much attention. Substantial reduction in fuel cost could be obtained by the application of modern heuristic optimization techniques for scheduling of the committed generator units. In this study, two cases are taken named three-unit system and six-unit system. The fuel cost for both systems compared using conventional lambda-iteration method and PSO method. These calculations are done for without transmission loss as well as with transmission losses. In the end, the fuel cost for both methods compared to analyze the better one from them. All the analyses are executed in MATLAB environment.

Deep learning architectures have yielded a significant leap in target detection performance. However, the high cost of deep learning impedes real-world applications, especially for UAV and UGV platforms. Moreover, detecting small targets is still of lower accuracy in contrast to the large ones. Aiming to comprehensively handle these two issues, a novel SP-CBAM-YOLOv5 architecture is proposed. The main novelty of our hybrid model lies in the cooperation of the attention mechanism and the typical YOLOv5 architecture, which can largely improve the performance of the small target detection. Moreover, the depth convolution and knowledge distillation are jointly introduced for lightening the model architecture. To evaluate the performance of our proposed SP-CBAM-YOLOv5, we built a novel dataset containing challenging scenes of power engineering. Experimental results on this benchmark demonstrate that our proposed SP-CBAM-YOLOv5 achieves a competitive performance in contrast to the other YOLO architectures. Besides, our lightweight YOLOv5 has more than 70% decrease of parameters. Moreover, the ablation study is conducted to demonstrate the compact architecture of SP-CBAM-YOLOv5.

In the process of multi-UAVs cooperative reconnaissance operations, due to the limited battery capacity and computing resources of the unmanned aerial vehicle (UAV), processing tasks can not only lead to excessive delay, but also increase the energy consumption of the UAV, which reduces the endurance time of the UAV. Therefore, we have proposed a mobile edge computing (MEC) system architecture composed of single unmanned helicopter (UH) and multiple reconnaissance UAVs. Among them, the UH as a MEC server to provide computing services for reconnaissance UAVs. By solving the computing offloading strategy problem of multi-UAVs, the objective is to minimize the weighted sum of energy consumption and delay for the multi-UAVs' task execution. In solving the problem, previous heuristic algorithms such as the Particle Swarm Optimization (PSO) are often used as basic algorithms for research, but they tend to converge early, fall into local optimum easily, and have low solution accuracy, making it difficult to obtain the optimal offloading strategy. Therefore, this paper proposes an improved bat algorithm (IBA) with fast convergence ability and global search ability. Through the simulation experiments and comparative analysis of PSO, BA, IPSO and IBA, it is proved that the IBA is more accurate, stable, and efficient in solving this problem based on the system architecture proposed in this paper, and effectively reduces the weighted sum of energy consumption and delay for the multi-UAVs' task execution.